Keywords: Analysis/Processing, Myocardium, Cardiac Mapping, Multi-Task Learning
Motivation: Motion Correction (MoCo) of free-breathing cardiac T1 mapping is imperative yet challenging due to the variable image contrast and large respiratory motion.
Goal(s): To propose a deep learning-based joint motion correction, reconstruction, and segmentation method for free-breathing cardiac T1 mapping.
Approach: The method exploits the inter-task dependency via physical and anatomical constraints, and employs a Coarse-to-fine Diffeomorphic Groupwise Registration Network (CDGR-Net) for motion correction. We evaluated the method on a public cardiac T1 mapping dataset.
Results: Our method outperformed alternative MoCo methods in registration accuracy and mapping quality, and provided accurate segmentation of the motion-corrected T1 maps.
Impact: The proposed method is expected to promote the clinical usability and reliability of cardiac T1 mapping, especially for patients who cannot tolerate repeated breath-holding MRI scans.
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